chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,141 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import pytest
+
+import tvm
+import tvm.testing
+from tvm import relax
+from tvm.ir import Op
+from tvm.script import relax as R
+
+
+def test_op_correctness():
+    g = relax.Var("g", R.Tensor((3, 10, 10), "float32"))
+    x = relax.Var("x", R.Tensor((3, 5, 10, 10), "float32"))
+    y = relax.Var("y", R.Tensor((3, 10, 10), "int64"))
+    w = relax.Var("w", R.Tensor((5,), "float32"))
+    assert relax.op.grad.nll_loss_backward(g, x, y, w).op == Op.get("relax.grad.nll_loss_backward")
+
+    g = relax.Var("g", R.Tensor((3, 3, 8, 8), "float32"))
+    x = relax.Var("x", R.Tensor((3, 2, 10, 10), "float32"))
+    assert relax.op.grad.max_pool2d_backward(g, x, (3, 3)).op == Op.get(
+        "relax.grad.max_pool2d_backward"
+    )
+    assert relax.op.grad.avg_pool2d_backward(g, x, (3, 3)).op == Op.get(
+        "relax.grad.avg_pool2d_backward"
+    )
+    g = relax.Var("g", R.Tensor((3, 2, 5), "float32"))
+    x = relax.Var("x", R.Tensor((3, 4, 5), "float32"))
+    indices = relax.Var("indices", R.Tensor((2,), "float32"))
+    assert relax.op.grad.take_backward(g, x, indices, axis=1).op == Op.get(
+        "relax.grad.take_backward"
+    )
+    assert relax.op.grad.no_grad(x).op == Op.get("relax.grad.no_grad")
+    assert relax.op.grad.no_grad(x).args[0] == x
+    assert relax.op.grad.start_checkpoint(x).op == Op.get("relax.grad.start_checkpoint")
+    assert relax.op.grad.start_checkpoint(x).args[0] == x
+    assert relax.op.grad.end_checkpoint(x).op == Op.get("relax.grad.end_checkpoint")
+    assert relax.op.grad.end_checkpoint(x).args[0] == x
+
+
+def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_ty: relax.Type):
+    ret = bb.normalize(call)
+    tvm.ir.assert_structural_equal(ret.ty, expected_ty)
+
+
+def test_start_checkpoint_input_not_var():
+    bb = relax.BlockBuilder()
+    x = relax.Var("x", R.Tensor((3, 4), "float32"))
+    y = relax.Var("y", R.Tensor((3, 4), "float32"))
+
+    # ok because x + y will be normalized into a relax Var
+    with bb.function("main", [x, y]):
+        gv = bb.emit(relax.op.grad.start_checkpoint(x + y))
+        bb.emit_func_output(gv)
+
+    # wrong: tuple will not be normalized
+    with pytest.raises(TypeError):
+        bb.normalize(relax.op.grad.start_checkpoint((x, y)))
+
+    # wrong: const will not be normalized
+    with pytest.raises(TypeError):
+        bb.normalize(relax.op.grad.start_checkpoint(relax.const(1, "float32")))
+
+
+def test_end_checkpoint_input_not_var():
+    bb = relax.BlockBuilder()
+    x = relax.Var("x", R.Tensor((3, 4), "float32"))
+    y = relax.Var("y", R.Tensor((3, 4), "float32"))
+
+    # ok because x + y will be normalized into a relax Var
+    with bb.function("main", [x, y]):
+        gv = bb.emit(relax.op.grad.end_checkpoint(x + y))
+        bb.emit_func_output(gv)
+
+    # wrong: tuple will not be normalized
+    with pytest.raises(TypeError):
+        bb.normalize(relax.op.grad.end_checkpoint((x, y)))
+
+    # wrong: const will not be normalized
+    with pytest.raises(TypeError):
+        bb.normalize(relax.op.grad.end_checkpoint(relax.const(1, "float32")))
+
+
+def test_nll_loss_backward_infer_ty():
+    bb = relax.BlockBuilder()
+
+    g = relax.Var("g", R.Tensor((3, 10, 10)))
+    x = relax.Var("x", R.Tensor((3, 5, 10, 10), "float32"))
+    y = relax.Var("y", R.Tensor((3, 10, 10), "int64"))
+    w = relax.Var("w", R.Tensor((5,), "float32"))
+
+    _check_inference(bb, relax.op.grad.nll_loss_backward(g, x, y), x.ty)
+    _check_inference(bb, relax.op.grad.nll_loss_backward(g, x, y, w), x.ty)
+
+
+def test_max_pool2d_backward_infer_ty():
+    bb = relax.BlockBuilder()
+
+    g = relax.Var("g", R.Tensor((3, 3, 8, 8), "float32"))
+    x = relax.Var("x", R.Tensor((3, 2, 10, 10), "float32"))
+
+    _check_inference(bb, relax.op.grad.max_pool2d_backward(g, x, (2, 2)), x.ty)
+    _check_inference(bb, relax.op.grad.max_pool2d_backward(g, x, (3, 3)), x.ty)
+
+
+def test_avg_pool2d_backward_infer_ty():
+    bb = relax.BlockBuilder()
+
+    g = relax.Var("g", R.Tensor((3, 3, 8, 8), "float32"))
+    x = relax.Var("x", R.Tensor((3, 2, 10, 10), "float32"))
+
+    _check_inference(bb, relax.op.grad.avg_pool2d_backward(g, x, (2, 2)), x.ty)
+    _check_inference(bb, relax.op.grad.avg_pool2d_backward(g, x, (3, 3)), x.ty)
+
+
+def test_take_backward_infer_ty():
+    bb = relax.BlockBuilder()
+
+    g = relax.Var("g", R.Tensor((3, 2, 5), "float32"))
+    x = relax.Var("x", R.Tensor((3, 4, 5), "float32"))
+    indices = relax.Var("indices", R.Tensor((2,), "float32"))
+
+    _check_inference(bb, relax.op.grad.take_backward(g, x, indices, axis=1), x.ty)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()